'''simple docstring'''




import warnings
from typing import List, Optional, Union

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType




class        a__ (  UpperCAmelCase__					):
     lowerCamelCase    :	Optional[int]				=["image_processor", "tokenizer"]
     lowerCamelCase    :	List[str]				="ViltImageProcessor"
     lowerCamelCase    :	List[Any]				=("BertTokenizer", "BertTokenizerFast")






     def __init__(					self						:						Optional[int]    ,							a						:						Tuple=None    ,							a						:						Any=None    ,							**a						:						Tuple       ):





         """simple docstring"""



         __lowerCamelCase            =    None
         if "feature_extractor" in kwargs:
             warnings.warn(
                 '''The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'''
                 ''' instead.'''    ,							a    ,							)
             __lowerCamelCase            =    kwargs.pop('''feature_extractor'''       )

         __lowerCamelCase            =    image_processor if image_processor is not None else feature_extractor
         if image_processor is None:
             raise ValueError('''You need to specify an `image_processor`.'''       )
         if tokenizer is None:
             raise ValueError('''You need to specify a `tokenizer`.'''       )

         super().__init__(a    ,							a       )
         __lowerCamelCase            =    self.image_processor






     def __call__(					self						:						List[str]    ,							a						:						Optional[int]    ,							a						:						Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None    ,							a						:						bool = True    ,							a						:						Union[bool, str, PaddingStrategy] = False    ,							a						:						Union[bool, str, TruncationStrategy] = None    ,							a						:						Optional[int] = None    ,							a						:						int = 0    ,							a						:						Optional[int] = None    ,							a						:						Optional[bool] = None    ,							a						:						Optional[bool] = None    ,							a						:						bool = False    ,							a						:						bool = False    ,							a						:						bool = False    ,							a						:						bool = False    ,							a						:						bool = True    ,							a						:						Optional[Union[str, TensorType]] = None    ,							**a						:						Optional[Any]    ,							):





         """simple docstring"""



         __lowerCamelCase            =    self.tokenizer(
             text=a    ,							add_special_tokens=a    ,							padding=a    ,							truncation=a    ,							max_length=a    ,							stride=a    ,							pad_to_multiple_of=a    ,							return_token_type_ids=a    ,							return_attention_mask=a    ,							return_overflowing_tokens=a    ,							return_special_tokens_mask=a    ,							return_offsets_mapping=a    ,							return_length=a    ,							verbose=a    ,							return_tensors=a    ,							**a    ,							)
         # add pixel_values + pixel_mask
         __lowerCamelCase            =    self.image_processor(a    ,							return_tensors=a       )
         encoding.update(a       )

         return encoding






     def   SCREAMING_SNAKE_CASE__  (					self						:						Union[str, Any]    ,							*a						:						List[str]    ,							**a						:						Union[str, Any]       ):





         """simple docstring"""



         return self.tokenizer.batch_decode(*a    ,							**a       )






     def   SCREAMING_SNAKE_CASE__  (					self						:						List[str]    ,							*a						:						Any    ,							**a						:						List[str]       ):





         """simple docstring"""



         return self.tokenizer.decode(*a    ,							**a       )






     @property
     def   SCREAMING_SNAKE_CASE__  (					self						:						Any       ):





         """simple docstring"""



         __lowerCamelCase            =    self.tokenizer.model_input_names
         __lowerCamelCase            =    self.image_processor.model_input_names
         return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names       )       )






     @property
     def   SCREAMING_SNAKE_CASE__  (					self						:						List[str]       ):





         """simple docstring"""



         warnings.warn(
             '''`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.'''    ,							a    ,							)
         return self.image_processor_class






     @property
     def   SCREAMING_SNAKE_CASE__  (					self						:						List[str]       ):





         """simple docstring"""



         warnings.warn(
             '''`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.'''    ,							a    ,							)
         return self.image_processor

'''simple docstring'''




def __lowerCAmelCase					(				UpperCamelCase__							,    UpperCamelCase__							)      ->       float:
    if digit_amount > 0:
        return round(number - int(UpperCamelCase__							)							,    UpperCamelCase__							)
    return number - int(UpperCamelCase__							)


if __name__ == "__main__":
  print(decimal_isolate(1.53, 0))
  print(decimal_isolate(35.345, 1))
  print(decimal_isolate(35.345, 2))
  print(decimal_isolate(35.345, 3))
  print(decimal_isolate(-14.789, 3))
  print(decimal_isolate(0, 2))
  print(decimal_isolate(-14.123, 1))
  print(decimal_isolate(-14.123, 2))
  print(decimal_isolate(-14.123, 3))
